{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 随机森林实战：使用随机数据实现分类",
   "id": "6e92feae3b7fce1c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1. 导入必要的库",
   "id": "f1d00f1e40c43b57"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T01:48:17.778461Z",
     "start_time": "2025-08-09T01:48:17.399420Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "import seaborn as sns"
   ],
   "id": "dccaa72b6ba1fad",
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 2. 生成随机数据集\n",
    "使用make_classification函数生成一个简单的二分类数据集"
   ],
   "id": "e7afd9398a8bf14c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T01:59:15.535350Z",
     "start_time": "2025-08-09T01:59:15.529373Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 生成随机数据\n",
    "X, y = make_classification(\n",
    "    n_samples=1000,  # 样本数量1000\n",
    "    n_features=5,  # 特征数量5\n",
    "    n_informative=3,  # 有效特征数量3\n",
    "    n_redundant=1,  # 冗余特征数量1\n",
    "    n_classes=2,  # 二分类问题\n",
    "    random_state=42  # 随机数种子，保证每次生成的数据集相同，实现可复现\n",
    ")"
   ],
   "id": "e9c18a77a5f34fe4",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T01:59:04.625645Z",
     "start_time": "2025-08-09T01:59:04.620171Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# X.shape\n",
    "X"
   ],
   "id": "1115c6e978eb2689",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.34146051, -0.99823279,  1.05194753, -2.32244879, -0.05434874],\n",
       "       [ 0.98351368, -0.86450116,  0.6118148 ,  1.06401099,  1.08775033],\n",
       "       [-1.67227626,  2.00818308,  0.55617131, -0.53199532, -1.08375031],\n",
       "       ...,\n",
       "       [-0.81758906,  0.56505355, -0.24905588,  1.03430797,  1.28374251],\n",
       "       [ 0.23000505,  0.31186216, -0.36660583,  0.85488488,  0.33894729],\n",
       "       [-1.58345464,  2.43439504,  0.45088763, -0.40602453, -1.4977121 ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T01:52:54.418108Z",
     "start_time": "2025-08-09T01:52:54.412070Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 划分数据集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ],
   "id": "b138af876599bee0",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T01:58:56.954733Z",
     "start_time": "2025-08-09T01:58:56.844281Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可视化训练集和数据集\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=50, cmap='rainbow')\n",
    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=50, cmap='rainbow', alpha=0.5)\n",
    "plt.show()"
   ],
   "id": "913f76e1fea92318",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3. 训练随机森林模型",
   "id": "d3578054cdb87cdf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T02:17:35.160706Z",
     "start_time": "2025-08-09T02:17:35.020355Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 初始化随机森林分类器\n",
    "rfc = RandomForestClassifier(\n",
    "    n_estimators=100,  # 树的数量\n",
    "    max_depth=5,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# 训练模型\n",
    "rfc.fit(X_train, y_train)\n",
    "y_pred = rfc.predict(X_test)\n",
    "print('训练集准确率：', rfc.score(X_train, y_train))\n",
    "print('测试集准确率：', rfc.score(X_test, y_test))"
   ],
   "id": "80dd877992e8d9cf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确率： 0.9757142857142858\n",
      "测试集准确率： 0.9466666666666667\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T02:20:14.987485Z",
     "start_time": "2025-08-09T02:20:13.665164Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 初始化随机森林分类器\n",
    "rfc = RandomForestClassifier(\n",
    "    n_estimators=1000,\n",
    "    max_depth=10,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# 训练模型\n",
    "rfc.fit(X_train, y_train)\n",
    "y_pred = rfc.predict(X_test)\n",
    "print('训练集准确率：', rfc.score(X_train, y_train))\n",
    "print('测试集准确率：', rfc.score(X_test, y_test))"
   ],
   "id": "d3551613233caed",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确率： 1.0\n",
      "测试集准确率： 0.94\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4. 模型评估",
   "id": "c18065655b539853"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T02:21:59.854323Z",
     "start_time": "2025-08-09T02:21:59.688642Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 解决中文乱码的问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置黑体（Windows常用）\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# 打印分类报告\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"\\n模型准确率: {accuracy:.2f}\")\n",
    "\n",
    "# 混淆矩阵\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('混淆矩阵')\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.show()"
   ],
   "id": "a3cf7e6e1a1a1923",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.94       144\n",
      "           1       0.95      0.94      0.94       156\n",
      "\n",
      "    accuracy                           0.94       300\n",
      "   macro avg       0.94      0.94      0.94       300\n",
      "weighted avg       0.94      0.94      0.94       300\n",
      "\n",
      "\n",
      "模型准确率: 0.94\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 5. 特征重要性可视化",
   "id": "af9b7b09ad76e9fa"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T02:24:12.130398Z",
     "start_time": "2025-08-09T02:24:11.992146Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 获取特征重要性\n",
    "feature_importance = rfc.feature_importances_\n",
    "\n",
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(X.shape[1]), feature_importance, align='center')\n",
    "plt.xticks(range(X.shape[1]), [f'特征 {i}' for i in range(X.shape[1])])\n",
    "plt.title('特征重要性')\n",
    "plt.ylabel('重要性得分')\n",
    "plt.show()"
   ],
   "id": "4443d3154b9c511c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 8. 调整树的数量观察效果",
   "id": "76c87805dccfb929"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T04:17:07.137727Z",
     "start_time": "2025-08-09T04:17:06.403362Z"
    }
   },
   "cell_type": "code",
   "source": [
    "n_trees = [1, 5, 10, 50, 100, 200]\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "for n in n_trees:\n",
    "    rf_temp = RandomForestClassifier(n_estimators=n, random_state=42)\n",
    "    rf_temp.fit(X_train, y_train)\n",
    "    train_acc.append(accuracy_score(y_train, rf_temp.predict(X_train)))\n",
    "    test_acc.append(accuracy_score(y_test, rf_temp.predict(X_test)))\n",
    "\n",
    "# 绘制结果\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(n_trees, train_acc, label='训练准确率', marker='o')\n",
    "plt.plot(n_trees, test_acc, label='测试准确率', marker='o')\n",
    "plt.xlabel('树的数量')\n",
    "plt.ylabel('准确率')\n",
    "plt.title('树的数量对模型性能的影响')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "id": "7dbab24223feea04",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "9ffc6c511517c4dd"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "c651cf39b9322bd7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T02:00:35.789736Z",
     "start_time": "2025-08-09T02:00:35.786319Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "891fc99dc79b01b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "847961aacb6584e2"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
